The hyper-geometric distribution model (HGDM) has been presented as a software reliability growth model with the capability to make estimations for various kinds of real observed test-and-debug data. With the HGDM, the discovery and rediscovery of faults during testing and debugging has been discussed. One of the parameters, the 'ease of test' function w(i) represents the total number of faults discovered and rediscovered at a test instance. In this paper, the authors firstly show that the ease of test function w(i) can be expressed as a function of the test coverage for the software under test. Test coverage represents a measure of how much of the software has been tested during a test run. Furthermore, the ease of test function w(i) can integrate a user-experience based parameter c that represents the strength of test cases to discover faults. This parameter c allows the integration of information on the goodness of test cases into the estimation process. The application of the HGDM to a set of real observed data clearly shows that the test coverage measure can be integrated directly into the ease of test function w(i) of the model.>

The hyper-geometric distribution model (HGDM) has been proposed for estimating the number of faults initially resident in a program at the beginning of the test/debug process. However, the parameters of the hyper-geometric distribution necessary for making the estimation were previously determined by the 3-dimensional exhaustive search and therefore, much time was needed to get the numerical result. The authors demonstrate, using real test/debug data of programs, that the least square sum method can be well applied to the estimation of such parameters of the hyper-geometric distribution model. Thus, the time needed for calculating the estimates can be reduced greatly.>

Six ways to estimate parameters of the hypergeometric distribution are investigated, and their accuracies are examined comparatively. It is demonstrated that the least-squares sum method is the best one among those tried, and can be applied to real test/debug data for estimating the number of faults still resident in a program after test/debugging. By this method the estimation time can be reduced greatly.>

The hyper-geometric distribution is used to estimate the number of initial faults residual in software at the beginning of the test-and-debug phase. The hyper-geometric distribution growth model (HGD model) is well suited to making estimates for the observed growth curves of the accumulated number of detected faults. The advantage of the proposed model is the applicability to all kinds of observed data. By application of a single model, exponential growth curves as well as S-shaped growth curves can be estimated. The precise formulation of the HGD model is presented. The exact relationship of this model to the NHPP Goel-Okumoto growth model and the delayed S-shaped growth model is shown. With the introduction of a variable fault detection rate, the goodness of fit of the estimated growth curve to the growth curve of real observed faults is increased significantly. Different examples of the applicability of the model to real observed data are presented.>

It is shown that the hyper-geometric distribution model can be applied to different types of test-and-debug data to estimate the number of initial software faults. Examples show that the fitness of the estimated growth curves of the model to real data is satisfactory. The relationship of the model to those proposed earlier is clarified. Some of them can be expressed as a special case of the proposed model. The S-shaped characteristic of the growth curve can also be introduced using this model.>